Information retrieval from a collection of information objects tagged with hierarchical keywords

ABSTRACT

The present invention can include a data processing system-implemented method or a data processing system readable media having software code for carrying out the method. The method can comprise formulating queries, searching for a plurality of information objects, or a combination thereof. In a specific embodiment, an original query with at least one keyword can be automatically expanded to an expanded query that includes at least one keyword that is not in the original query. The expanded query may be used to search for information objects that are relevant to the expanded query.

BACKGROUND OF INVENTION

1. Field of the Invention

This invention relates in general to methods and data processing systemreadable media, and more particularly, to data processingsystem-implemented methods of formulating queries and searching for aplurality of information objects and data processing system readablemedia having software code for carrying out those methods.

2. Description of the Related Art

A goal of information retrieval systems is to allow efficient access toselected documents or other kinds of information objects from arepository. The user of such a system may be interested in knowing theexistence and location of the available information objects that arerelevant to a specific request or query.

A common approach used in information retrieval systems is to associateone or more keywords with each information object. The set of all knownkeywords comprises the “master set” of keywords. To form a query, theuser provides one or more keywords, which may or may not be drawn fromthe master set. The information retrieval system then returns eachinformation object for which one or more of its associated keywordsmatches one or more of the keywords in the query. As a further step, amathematical formula can be applied to the number of keyword matches toprovide a scalar that is associated with each information objectreturned by the query. The scalar serves as a “relevance score” thatindicates the degree to which the particular information object matchesthe query. This approach can be generally termed “keyword-matching” andthere are many specific embodiments used in practice. Some difficultieswith the keyword-matching approach are set forth in the followingparagraphs.

First, the user of the system may not know or be able to grasp all ofthe possible keywords in the master set. In this case, the user mayprovide queries that contain keywords that are not used in the masterset. This reduces the effectiveness of the system, particularly when themaster set includes keywords that have closely related meanings in aparticular application, and a simple match cannot make use of thisinformation. For example, assume the repository contains documentsdescribing fruits and vegetables, and a treatise on tomatoes has beenassigned the keyword “nightshade” because it also includes discussionsof eggplant and potatoes. The user desiring information on tomatoesmight enter a query such as “tomatoes” and this query would fail tomatch the treatise on the nightshade family, even though that documentis relevant to the user's purpose.

Second, the mathematical formulae that are widely described and used tocompute relevance scores may not take advantage of the relationshipsamong keywords that are inherent in any specific information repository.For example, given a repository that contains documents on fruits andvegetables, systems that compute a relevance score based only on thenumber of keyword matches have no way to incorporate the fact that adocument tagged with keywords “nightshade” and “treatise” should moreclosely match the query pair “tomato” and “treatise” than the query pair“lamp” and “treatise.” Attempts to address these shortcomings have beenproposed, but the methods fail to fully address the problems users mayencounter. Some systems have been developed that organize the keywordsinto a hierarchical tree structure. This, by itself, is not a solution,as will become evident in some of the paragraphs that follow.

A system described in U.S. Pat. No. 6,094,652 (“Faisal”) places keywordsinto a hierarchical structure. The hierarchy expresses the associationsamong the keywords in the repository. When responding to a user query,the system suggests keywords from the hierarchy that broaden or narrowthe scope. The system also suggests keywords that represent conceptsthat are neither broader nor narrower but are related by means of anexplicit cross-link among the nodes in the keyword hierarchy. The usercan refine his or her query in an interactive and iterative fashion.

A system described in U.S. Pat. No. 6,098,066 (“Snow”) arranged theinformation objects into a document hierarchy (a tree data structure).Each node of the hierarchy corresponds to a category and contains atleast one document. The user of the system has the option of restrictingtheir search to the documents branching from a specific category (whichthese authors term a “directed” search) or searching all documents inthe repository (which these authors term an “undirected” search). Theuser may restrict the number of documents returned by the system byfocusing on a particular category, while leaving the user with theoption of searching the entire repository if desired.

A system described in U.S. Pat. No. 5,991,756 (“Wu”) places documentsinto a hierarchical structure. The system retrieves documents that matchone or more query keywords directly or match “indirectly” by beinglocated as a child node to a document in the document hierarchy thatmatches directly one or more of the query terms.

A system described in U.S. Pat. No. 5,630,125 (“Zellweger”) placesdocuments into a hierarchical structure that has one or more pathsleading to a given document. The system provides an interactive methodthat allows the user to formulate a final query by navigating thehierarchy structure to the desired documents. Multiple paths supportsynonyms and allow the user to clarify word meaning in a given context.

A system described in U.S. Pat. No. 5,787,417 (“Hargrove”) is highlysimilar to that described by Zellweger in that it provides an interfacefor allowing the user to interactively navigate the hierarchy of therepository to locate the desired information objects.

A textbook by C. J. Van Rijsbergen (Information Retrieval, 2 ^(nd) Ed)describes a general strategy for information retrieval by keywordmatching. It also gives the mathematical formulae that can be used totransform the combination of a “query vector” and a “document vector”into a final “relevance score” that can be used to rank the documentsreturned by a retrieval system according to their degree of relevance tothe query.

Each of the systems in those documents has at least one limitation ordisadvantage in some applications.

Systems that require the user to interactively refine their query (suchas those described by Faisal, Zellweger, and Hargrove) are inherentlymore time consuming for the user than a system that returns results inresponse to a single query. Further, human interfacing with a computercosts a company valuable human resources. In some applications (such asthose described in the next section), the information retrieval isautomated, and there is no opportunity to refine or otherwise change thequery before searching begins.

Systems that restrict the retrieved documents to those with a particularancestry in a document hierarchical structure (such as those describedby Faisal, Snow, and Wu) can fail to return relevant documents outsidetheir hierarchical search path unless there have been many cross-linksprovided (such as in the system described by Faisal). Cross links mustbe created and maintained manually, a time-consuming and error-proneprocess.

Several of the prior systems do not prescribe a method for assigning arelevance score between the query and the documents in the repository(such as the systems described by Zellweger and Hargrove). It is oftenconvenient for the users to have a relevance score to help them estimatetheir level of interest in the returned documents. Furthermore, systemsthat restrict the search path to a particular set of child nodes in thehierarchy (such as that described by Wu) cannot provide relevance scoresfor documents that lie outside the restricted set of child nodes. Insome applications, this means that not all documents can be assigned arelevance score in response to a given query.

SUMMARY OF INVENTION

Embodiments of the present invention do not suffer from the problemsseen with prior art methods and systems. A user is not required tointeractively refine a search because a data processing system can beprogrammed to automatically expand an original query having originalkeywords to an expanded query that includes friend keywords of theoriginal keywords. Searching may performed that cover parts of one ormore hierarchies because keywords outside a specific ancestry may beused. Further, the documents are not required to be placed within adocument hierarchy. A cross-link system is not required, which savesvaluable money and human resources.

In one set of embodiments, a data processing system-implemented methodof searching for a plurality of information objects can comprisereceiving a first signal that includes or is used to form a first query.The first query may include a first keyword within a hierarchy. Themethod can also comprise expanding the first query to a second query.The second query may includes the first keyword and a second keywordwithin the hierarchy. The method can further comprise searching thedatabase using the second query and finding a first identifier for afirst information object that corresponds to the second query.

In another set of embodiments, a data processing system-implementedmethod of formulating a query can comprise receiving a first signal thatincludes or is used to form a first query having a first keyword. Themethod can also comprise automatically expanding the first query to asecond query. The second query may include the first keyword and asecond keyword that is not present within the first query.

In still other embodiments, a data processing system readable medium canhave code embodied within it. The code can include instructionsexecutable by a data processing system. The instructions may beconfigured to cause the data processing system to perform the methodsdescribed herein.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of theinvention, as defined in the appended claims.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate embodiments of the invention andtogether with the description, serve to explain the principles on of theinvention.

FIG. 1 includes an illustration of a hardware architecture for carryingout methods of searching a database;

FIG. 2 includes an illustration of a data processing system storagemedium including software code;

FIG. 3 includes an illustration of a hierarchy of keywords;

FIG. 4 includes a flow diagram for adding keywords, friends, andinformation objects to a database; and

FIG. 5 includes a flow diagram for obtaining information objects relatedto a keyword and its friends.

Skilled artisans appreciate that elements in the figures are illustratedfor simplicity and clarity and have not necessarily been drawn to scale.For example, the dimensions of some of the elements in the figures maybe exaggerated relative to other elements to help to improveunderstanding of embodiments of the present invention.

DETAILED DESCRIPTION

Reference is now made in detail to the exemplary embodiments of theinvention, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts (elements).

The present invention can include a data processing system-implementedmethod or a data processing system readable media having software codefor carrying out the method. The method can comprise formulatingqueries, searching for a plurality of information objects, or acombination thereof. In a specific embodiment, an original query with atleast one keyword can be automatically expanded to an expanded querythat includes at least one keyword that is not in the original query.The expanded query may be used to search for information objects thatare relevant to the expanded query.

Before discussing embodiments of the present invention, a hardwarearchitecture for using embodiments is described. FIG. 1 illustrates anexemplary architecture and includes a client computer 12 that isbi-directionally coupled to a network 14, and a server computer 16 thatis bi-directionally coupled to the network 14 and database 18. Theclient computer 12 includes a central processing unit (“CPU”) 120, aread-only memory (“ROM”) 122, a random access memory (“RAM”) 124, a harddrive (“HD”) or storage memory 126, and input/output device(s) (“I/O”)128. The I/O devices 128 can include a keyboard, monitor, printer,electronic pointing device (e.g., mouse, trackball, etc.), or the like.The server computer 16 can include a CPU 160, ROM 162, RAM 164, HD 166,and I/O 168.

Each of the client computer 12 and the server computer 16 are examplesof data processing systems. ROM 122 and 162, RAM 124 and 164, HD 126 and166, and the database 10 include media that can be read by the CPU 120or 160. Therefore, each of these types of memories includes a dataprocessing system readable medium. These memories may be internal orexternal to the computers 12 and 14.

The methods described herein may be implemented in suitable softwarecode that may reside within ROM 122 or 162, RAM 124 or 164, or HD 126 or166. In addition to those types of memories, the instructions in anembodiment of the present invention may be contained on a data storagedevice with a different data processing system readable storage medium,such as a floppy diskette. FIG. 2 illustrates a combination of softwarecode elements 204, 206, and 208 that are embodied within a dataprocessing system readable medium 202, on a floppy diskette 200.Alternatively, the instructions may be stored as software code elementson a DASD array, magnetic tape, conventional hard disk drive, electronicread-only memory, optical storage device, CD ROM or other appropriatedata processing system readable medium or storage device.

In an illustrative embodiment of the invention, the computer-executableinstructions may be lines of compiled C⁺⁺, Java, or other language code.Other architectures may be used. For example, the functions of theclient computer 12 may be incorporated into the server computer 16, andvice versa. FIGS. 4 and 5 include illustrations, in the form offlowcharts, of the structures and operations of such a software program.

Communications between the client computer 12 and the server computer 16can be accomplished using electronic or optical signals. When a user(human) is at the client computer 12, the client computer 12 may convertthe signals to a human understandable form when sending a communicationto the user and may convert input from a human to appropriate electronicor optical signals to be used by the client computer 12 or the servercomputer 16.

Attention is now directed to data preparation and system initializationfor searching. During data preparation, a master list of keywords(referred to as the Master Keyword List) is generated and arranged intoone or more sets of hierarchical relationships or “trees.” For example,FIG. 3 includes a tree (hierarchy) with 12 keywords: B, C, . . . , M.The root of the tree is the node A302. Nodes B312, C314, and D316 arethe children of node A302. Nodes 321 E321, F322, and G323 are thechildren of node B312. Nodes H326, I327, and J328 are the children ofnode D316. Nodes 332 K332, L334, and M336 are the children of node I327.Nodes C314, E321, F322, G323, H326, J328, K332, L334, and M336 have nochildren.

The dashed lines 310, 320, and 330 are used as a point of reference todivide “generations” of nodes. Each node belongs to a specific“generation” that is equal to the number of ancestor nodes between thegiven node and the root of the tree. For example, nodes 332, 334, and336 are members of the third generation (generation=3) because thesenodes have three ancestors (nodes 327, 316, and 302). The significanceof the generations will become apparent later. Note that FIG. 3 canallow for the identification of the “lowest common ancestor” node. Forexample, when comparing nodes 332 and 336, the lowest common ancestornode is node 327, which is the parent node for each of nodes 332 and336. When comparing nodes 326 and 336, the lowest common ancestor nodeis node 316, which is the parent node to node 326 and the grandparentnode of node 336.

The name of the tree can be the root, which in this example can be “treeA.” If tree A were the only tree, then {B, C, . . . , M} may also be themaster list of keywords. Each tree can be represented as a relationaldatabase table, as shown for this example in Table I. Additional tablesmay be present for other keyword hierarchies. TABLE 1 Keyword HierarchyTable Keyword/Node Parent Generation B A 1 C A 1 D A 1 E B 2 F B 2 G B 2H D 2 I D 2 J D 2 K I 3 L I 3 M I 3

Referring to FIG. 4, the keyword hierarchy is produced (circle 412) andstored at part of the Keyword Hierarchy Table 414 that may be withindatabase 18.

The database 18 may include a repository of information objects. Theinformation objects themselves may include documents, products,electronic discussion archives, code fragments, and any other computerrepresentations of knowledge or information. Each information object canhave a unique identifier, hereafter called the “object ID.” In addition,each information object may have other important properties, such as itslanguage, access control parameters, object type (document, softwareproduct, etc.), and the like.

A file or database table can specify a set of keywords relevant to eachinformation object, which will be referred to as the “Info ObjectKeyword Table.” All keywords related to the information objects shouldbe members of the Master Keyword List. In other words, keywords arerelated to information objects (circle 422) and can be stored as part ofthe Info Object-Keyword Table 424 as seen in FIG. 4. In one embodiment,a relevance rating for each keyword may be provided and can representthe degree of relevance between a keyword and an information object. Therelevance rating can be assigned by subject matter experts who assignkeywords to information objects and populate the information objectrepository. This data can be contained in a relational database tablewhere each row contains an object ID, a keyword (or keyword identifier),and a relevance rating (e.g., from 1 to 10), as shown for example inTable II. An object ID can appear multiple times in this table when morethan one keyword is considered relevant to the information object. TABLEII Relevance Between Object IDs and Keywords. Object Id Keyword Ratingobj 1 C 8 obj 1 F 7 obj 2 B 5 obj 2 H 9 obj 2 I 9 obj 3 E 10 obj 4 J 9obj 4 M 6 obj 5 F 7 obj 5 J 4 obj 5 L 8 obj 6 D 6 obj 6 C 8 obj 7 H 9obj 8 B 4 obj 8 D 8

Note that the relevance rating assigned to a given informationobject-keyword pair need not be the same as the rating assigned to adifferent information object-keyword pair, even if the keyword is thesame in both cases. In Table II, for example, the keyword J is relevantto information object “obj4” with relevance rating of 9, and to “obj5”with relevance rating of 4. This means that keyword J is more relevantto obj4 than to obj5.

The Keyword Hierarchy Tables may be used to produce a set of “friend”keywords (and corresponding association scores between a keyword-friendpair) for every keyword in the Master Keyword List. After akeyword-friend association score can be calculated (circle 432), andthat information may be stored in the Keyword-Friend Table 434 ofdatabase 18.

Attention is now directed to some of the details in determiningkeyword-friend association scores. A number of different methods can beused to determine the association scores between keywords and theirfriends. In one embodiment, the association score may be determinedusing a tree distance algorithm, further described below. The pairing ofkeywords with their friends can be maintained in the relational databasetable 424 (within database 18, for example), with an entry for eachkeyword/friend pair. Every keyword is a friend of itself, with themaximum possible association score. A keyword's other friends can begiven by further entries in the Keyword-Friend Table 434, one for eachdistinct pair of keywords, along with the association score for thatpair. An example consistent with FIG. 3 may include association scoresthat range from 1 to 10. As shown in Table III. TABLE III Keyword FriendTable Keyword Friend Distance Score B B 0 10 B E 2 8 B F 2 8 B G 2 8 C C0 10 C B 6 4 C D 6 4 D D 0 10 D H 2 8 D I 2 8 D J 2 8 E E 0 10 E B 2 8.F F 0 10 F B 2 8 G G 0 10 G B 2 8 H H 0 10 H D 2 8 I I 0 10 I K 1 9 I L1 9 I M 1 9 J J 0 10 J D 2 8 K K 0 10 K I 1 9 L L 0 10 L I 1 9 M M 0 10M I 1 9

Associated with each link in the keyword hierarchy (represented by thearrows in FIG. 3) is a weight. The weight is equal to the highestgeneration number in the tree minus the generation of the parent node inthe link. For example, the links between node I327 and its childrenK332, L334, and M336 have a weight equal to 1, which is the highestgeneration number in the tree (3) minus the generation of the parentnode/(2). The dotted horizontal lines in FIG. 3 indicate thegenerations, and line 310 can correspond to a weight of “3,” line 320can correspond to a weight of “2,” and line 330 can correspond to aweight of “1.”

The association score between any two keywords in the hierarchy may bedetermined in two acts:

-   -   1.compute the “tree distance” between the two keywords, then    -   2.transform the tree distance according to a mathematical        equation to get the final association score.

To compute the tree distance, the method can use the followingalgorithm. Trace the ancestry of each node up to the lowest commonancestor. For each link that is used to get to the lowest commonancestor, maintain a sum of the weights. One embodiment may use a sum ofthe weights squared. (In general, a user can define the tree distance tobe the sum of any bias function applied to the weights; power-laws maybe particularly useful.)

For example, let d(x,y) denote the tree distance between nodes x and y,where x and y are nodes in the tree. To compute d(L,H), a path can startat node L 334 and traces across dotted line 330 to its parent node I327,and then across dotted line 320 to its grandparent node D316. Node D316is the lowest common ancestor between nodes L334 and H326. Starting atnode H326, a path can reach node D316 by crossing line 312. Thus, thedistance can be calculated as (1+2) for going from node L328 to nodeD316, and adding 2 for the distance from node H326 to D316. In moreexplicit algebraic notation,d(L,H)=d(L,I)+d(I,H)d(L,H)=d(L,I)+(d(I,D)+d(H,D))d(L,H)=1+2+2d(L,H)=5.

The following are some more examples:d(L,M)=1+1=2d(L,H)=(1+2)+2=5d(L,C)=(1+2+3)+3=9d(H,C)=(2+3)+3=8d(E,C)=(2+3)+3=8d(H,E)=(2+3)+(2+3)=10d(L,E)=(1+2+3)+(2+3)=11

Other methods may be used to determine the tree distance. Betweensibling nodes (child nodes from a common parent node), a symmetricdistance matrix may be generated to determine scale of distances betweenthose sibling nodes. That is, the distance between any two children of aparent node can be determined by multiplying appropriate entry in thedistance matrix by the sum-of-weights distance.

For example, in FIG. 3, the designers of the tree may choose to define achild distance matrix for node D316 as the following: H I J H 1 1 1.5 I1 1 1 J 1.5 1 1

This matrix has two properties. First, the diagonal entries are allequal to one. Second, it is symmetric. The elements of the matrix can bedenoted by M(x,y), so that (for example) M(H,J)=1.5.

A method for calculating an association score between nodes can use thismatrix to scale the sum-of-weights distance between the child nodes ofthe lowest common ancestor. Using only the sum-of-weights distance onthe tree in FIG. 3 would produce the result d(L,H)=d(L,J)=5. Thechild-distance matrix allows the designers of the tree to express acloser relationship between some children than between others. In thisexample, nodes H326 and I327 are more closely related to each other thanto node J328 even though all three share the same parent.

In one example, the method can use the child-distance matrix to scalethe distance between children of node D to express the closerrelationship between nodes H326 and I327:d(L,H)=d(L,I)+M(I,H)*d(I,H)d(L,H)=d(L,I)+M(I,H)*(d(I,D)+d(H,D))d(L,H)=1+1*(2+2)d(L,H)=5.

Between nodes J328 and L 334, the calculation may be:d(L,J)=d(L,I)+M(I,J)*d(I,J)d(L,J)=d(L,I)+M(I,J)*(d(I,D)+d(J,D))d(L,J)=1+1*(2+2)d(L,J)=5.

While the matrix may usually be symmetric, symmetry is not required.

This procedure can achieve a desirable effect, in that it givesdesigners additional flexibility to define quantitative relationshipsamong the keywords in the hierarchy, and these relationships can be usedto provide superior information retrieval results. The associationscores may be determined automatically by server computer 16 based atleast in part upon positions of a keyword and its friend Keyword withinthe hierarchy.

The method can produce an association score between two keywords bycomputing a distance between the keywords (using the sum-of-weights orthe sum-of-weights plus child-distance matrix method) and then applyinga transform to give the highest association scores to those keywordswith the lowest values of the tree distance.

One embodiment can use the following transformation. Let C denote themaximum desired association score. The association score between anyobject and itself is equal to C. Let d denote the tree distance. Letf(d) denote a monotonically increasing function of the argument d, andint(f(d)) denote an integer value of f(d). Let s denote an associationscore corresponding to tree distance d, and can be given by:s=max(0, c−int(f(d)))

In this formula, the value of max (x,y) is the greater of the argumentsx and y and int(x) is the integer part of the argument x. The value “0”in the equation may be used so that s cannot be negative. In oneembodiment, the squares of the weights can be summed to obtain the treedistance d, f(d)=d**0.5 and C=10.

To determine the “friend” keywords for a given keyword, the method canbe used to compute the association scores between the given keyword andall other keywords in the hierarchy. The top N of the keywords with thegreatest association scores become the set of friend keywords that arestored in a relational database table within database 18. One embodimentcan use the keywords with the top 10 association scores to expand thequery keywords. A user or code in software or hardware can be set thevalue of N.

At this point in the process, data preparation and system initializationhas been completed. The appropriate information may be stored withdatabase 18 or other storage device having persistent memory.

Query processing can now be performed. It is in this second phase, queryprocessing, that information retrieval actually occurs. A query can be aset of keywords (or keyword IDs), generated by some specific end-useractivity for some particular application. The nature of suchapplications and specific examples are discussed below. The querykeywords are members of the Master Keyword List. By using a limitednumber of keywords from the Master Keyword List, searching can beperformed faster compared to free-form searching.

FIG. 5 includes a flow diagram of acts that can be performed whenprocessing a query. Note that some of the acts may be optional and notrequired for all implementations. The method can comprise receiving froma client computer 12, an original (first) query that includes a firstkeyword within a hierarchy (block 502). After the original query isreceived, the server computer 16 can retrieve all the friend keywords(and association scores) for each keyword in the original query. Asecond keyword from the Keyword-Friend Table 424 can be identified as afriend of the first keyword, although the second keyword may not havebeen a keyword within the first query.

The method further includes automatically expanding the original queryto an expanded (second) query that includes the first keyword and thesecond keyword within the hierarchy (block 522). In this specificexample, note that a third keyword may be present within the MasterKeyword List but is not part of the expanded query. The third keywordmay not be listed as a friend of the first keyword, or the third keywordmay not have had a sufficiently high enough association score comparedto other friend keywords of the first keyword.

The complete set of keywords from the original query and theircorresponding friend keywords can form the expanded query.

The association scores for the keyword-friend pairs may be used todetermine which friends to use but can also be used in relevance scoringthat will be described in more detail later.

Below is an example using the hierarchy in FIG. 3 and the associationscores in Table III. Original Query keywords: C K Expanded Querykeywords: C B D K I Association scores: 10 4 4 10 9

Keywords C and K can be examples of the first keywords, keywords B, D,and I can be examples of the second keywords, and keywords E, F, G, H,J, L, and M can be examples of the third keywords, which are not part ofthe expanded query.

After the expanded query has been generated, the method can includesearching the database 18 using the expanded query in block 542 of FIG.5. The method also can include finding identifiers for informationobjects that correspond to the expanded query (block 544). Morespecifically, the keywords in the expanded query can be used to identifya set of relevant information objects via the Info Object Keyword Table424. Only information objects with at least one keyword that is presentin the expanded query may be considered relevant to the query. TABLE IVObject identifiers, keywords, and relevance ratings. Relevancy Object IdKeyword Rating obj 1 C 8 obj 1 F 7 obj 2 B 5 obj 2 J 9 obj 2 I 9 obj 6 D6 obj 6 C 8 obj 8 B 4 obj 8 D 8

Table IV includes an exemplary set of relevant information objectscorresponding to the expanded query. The relevance rating can be arating of how relevant a keyword is to a specific information object.Note that obj8 is relevant to the expanded query but not to the originalquery because obj8 only includes keywords Band D(second keywords) asrelevant keywords.

Next, the method can calculate a relevance score for each of theidentified information objects (block 562). There are many possibleformulae for this calculation. In many instances, a weighted vector maybe used for the relevance score. The weighted vector can be a productbetween two vectors of dimensionality D, where D is the total number ofdistinct keywords in the master keyword list, and each vector element isthe relevance score for that keyword or zero if the keyword is absent.Users may find it useful to choose a formula which includes appropriatenormalization to account for variable parameters that should not affectthe final score spuriously, such as the number of keywords for a giveninformation object. This consideration suggests the following formulafor the relevance score R:$R = \frac{\sum\limits_{i = 1}^{M}{k_{i}q_{i}}}{K}$where:

-   -   {K} is the set of keywords associated with the information        object;    -   {k} is the set of relavance ratings for the information object        keywords in {K};    -   K=|{K}| is the number of keywords associated with the        information object;    -   {Q} is the set of keywords in the expanded query;    -   {q} is the set of association scores for the expanded query        keywords in {Q};    -   {M} is the set of keywords from the intersection of sets {K} and        {K};    -   M=|{M}| is the number keywords in the set {M} (i.e. the number        of matches);    -   {k_(i)}, i=1,2, . . . , M is the subset of {k} corresponding to        the elements of {K} in {M}; and    -   {q_(i)}, i=1,2, . . . , M is the subset of {q} corresponding to        the elements of {Q} in {M}.

The relevance score is determined as follows: find the sum, over eachexpanded query keyword that matches an information object keyword, ofthe product of the keyword's association score for the query and itsrelevance rating for the information object; and divide the sum by thenumber of keywords associated with the information object. This last actprovides appropriate normalization to avoid arbitrarily enhancing thescore of information objects that have a large number of keywords. Notethat it is not necessary to normalize by the number of query keywords,since this is a constant for a given query. Note that the equation giveabove is not the only way to determine a relevance score, and therefore,should not be construed as a limiting. Table V includes the relevancescores obtained in this fashion for the given set of eligibleinformation objects in Table IV and the expanded query. TABLE VRelevance Score Table. Relevance Object Id Score obj 1 40 obj 2 33.7 obj6 52 obj 8 24

The method may further include sorting the identified informationobjects based on the relevance scores (block 564). The list of eligibleinformation objects can be sorted from highest to lowest relevancescore. After sorting, the method may send the sorted information objectto the client computer (block 582). The resulting list should providesthe object IDs of all relevant information objects for the expandedquery, in order of relevance, based on the original query. Although thecalculating of the relevance score and sorting the identifiedinformation objects is optional, the information objects, sorted byrelevancy score aid the user at the client computer by indicating thedegree of relevance based on relevancy score. The final list for theexample of Table V can be given by Table VI. TABLE VI Final List ofSorted Information Objects. Relevance Object Id Score obj 6 52 obj 1 40obj 2 33.7 obj 8 24

The server computer 16 may send the client computer 12 the list seen inTable VI or a derivative of it. The information object Ids may includean alpha-numeric representation, a catalog number, or be replaced by atitle, or even the information object itself (or the first few words ofit) when information is seen by a user at client computer 16. Thereforesending an information object ID should be construed as including anyone or more of the pieces of information listed in this paragraph.

Filtering acts may be used as an optimal part of the method. Filteringmay select information objects by language, security level, length ofdocument, or the like. The user may define the filtering criterion. Thefiltering criterion may be sent from the client computer 12 to theserver computer 16 with the original query.

Other specific embodiments are presented to illustrate some of the otherfeatures of the keyword-friend query method and system. One embodimentmay serve as part of a suite of information retrieval systems for acorporate knowledge management system. The role of the method in thissystem is to provide employees, business partners, and customers of thecorporation with efficient access to information objects that arerelevant to a particular topic or user query. Access to the method andsystem can be through a software application made available on the WorldWide Web computer network via the HTTP communications protocol.

In this embodiment there may be three hierarchies: one hierarchy maycorrespond to the subject matter category addressed by the informationobject (“category hierarchy”), another hierarchy may correspond to theproject phase addressed by the information object (“phase hierarchy”),and still another hierarchy may correspond the role, or intendedaudience, of the information object (“role hierarchy”). Each hierarchyis described by a database table that lists the unique identifiers ofthe keywords in that hierarchy, along with the unique identifiers of theparent keywords. The keyword corresponding to the root of the tree mayhave no parent keyword. The translation from unique keyword identifierto the keyword text can be provided by means of another database table.

A further aspect of this embodiment can be a “keyword-type” weight valueassigned to each of the keyword hierarchies. This can allow some typesof keywords to be more significant than others, for example, a keywordof type “category” can be given a weight of 2 while “role” and “phase”keywords are given a weight of 1. All of the keywords in a givenhierarchy can have an identical keyword-type weight. This weight valuemay be separate and in addition to the association score of a specifickeyword for a specific information object, and can be used as anadditional multiplicative factor for each term in the summation used inthe relevance score.

In one application of this embodiment, a user at client computer 12 mayformulate a query by selecting one or more keywords from the threeavailable hierarchies. The keywords can be displayed on a screen andselected by selection boxes displayed on the user's HTML browser. Thekeywords he or she selects can then be submitted to the server computer16 that can search database 18 for the relevant information objects. Therelevant information objects that are returned can then be sent from theserver computer 16 and received by the client computer 12 where the usercan see the results.

In another application of this embodiment, the query may be formulatedautomatically according to the user's context on the Web site. By meansof a series of questions presented to the user as they navigate the Website, a query can be formulated based upon the question responses. Forexample, a series of question may reveal the user's interest in aparticular subject matter category, and the keyword corresponding tothat category can form the query that is submitted for processing. Therelevant information objects that are returned are then presented to theuser. Alternatively, the actions of the user at the website can betracked and information objects be presented to the user without havingto ask any questions or receive a query from the user.

Embodiments of the present invention have advantages over the prior artin the field of information retrieval. The methods can make use of therelationships among the keywords associated to each information object.These relationships can be expressed in the hierarchies to which thekeywords belong. By taking advantage of this information, the methodscan be used to find information objects in the repository that areclose, but not exact, matches to an original query.

The embodiments do not require interactive participation from the userbeyond the specification of the original query. The invention mayautomatically expand the scope of the original query to include keywordswith related meanings, so that matches can occur even on keywords theuser did not think to enter in the original query.

The methods may require only a few operations to compute a relevancescore for an information object. Therefore, the methods can be performedon a data processing system in a time efficient manner.

A further advantage seen with embodiments of the present invention isthe ability to create derivatives to address other problems or to beused in other fields. Direct mail, electronic mail, and the World WideWeb provide marketers with an opportunity to target product offers tospecific customers. These offers can include product recommendations.The process of constructing a product recommendation can be analogous tothe process of information retrieval. Therefore, the methods can be usedto provide product recommendations. The products (or more specificallyunique product codes) themselves can serve the role of keywords. Theproduct hierarchy maintained by many retailers can be used to establishthe relationships among the keywords. The form of a query may be a setof products in which a customer has expressed interest (throughpurchase, request for information, etc.). At least one of the methodspreviously described can be used to return a list of related informationobjects (which are product identifiers) to the customer as a kind ofproduct recommendation. Such recommendation may be valid under anassumption that consumers will be interested in products similar tothose that they have purchased or browsed in the past. Many retailersmaintain product hierarchies that express the similarity of items thatare closely related in a tree.

In the foregoing specification, the invention has been described withreference to specific embodiments. However, one of ordinary skill in theart appreciates that various modifications and changes can be madewithout departing from the scope of the present invention as set forthin the claims below. Accordingly, the specification and figures are tobe regarded in an illustrative rather than a restrictive sense, and allsuch modifications are intended to be included within the scope ofpresent invention.

Benefits, other advantages, and solutions to problems have beendescribed above with regard to specific embodiments. However, thebenefits, advantages, solutions to problems, and any element(s) that maycause any benefit, advantage, or solution to occur or become morepronounced are not to be construed as a critical, required, or essentialfeature or element of any or all the claims. As used herein, the terms“comprises,” “comprising,” or any other variation thereof, are intendedto cover a non-exclusive inclusion, such that a process, method,article, or apparatus that comprises a list of elements does not includeonly those elements but may include other elements not expressly listedor inherent to such process, method, article, or apparatus.

1-24. (canceled)
 25. A computer-implemented method of searching adatabase, comprising: receiving a first query with one or more keywords;automatically expanding the first query to a second query to include theone or more keywords and their friend keywords having association scoresthat meet or exceed a set value; searching the database using the secondquery; and identifying a set of information objects that correspond tothe second query.
 26. The method of claim 25, further comprising:determining a relevance score for each of the information objects thatcorrespond to the second query; and sorting the information objectsbased on their relevance scores.
 27. The method of claim 25, furthercomprising: determining the association scores based at least in partupon positions of the one or more keywords and their friend keywordswithin a hierarchy of keywords.
 28. The method of claim 25, furthercomprising: generating a list of keywords relevant to informationobjects residing in the database; and arranging the list of keywordsinto one or more hierarchies of keywords, each hierarchy defining arelationship between a keyword and its friend keywords within thehierarchy.
 29. The method of claim 28, further comprising: determining arelevance rating for each keyword in the list of keywords, wherein therelevance rating represents a degree of relevance between a keyword andan information object.
 30. The method of claim 25, further comprising:filtering at least some of the information objects residing in thedatabase to meet a defined criterion.
 31. A computer system programmedto implement the method according to claim
 25. 32. Acomputer-implemented method of formulating a query, comprising:receiving a first query with one or more keywords; and automaticallygenerating a second query that includes the one or more keywords andtheir friend keywords having association scores that meet or exceed aset value; wherein the association scores are determined based at leastin part upon positions of the one or more keywords and their friendkeywords within a keyword hierarchy.
 33. The method of claim 32, furthercomprising: searching a database having a repository of informationobjects; and identifying a set of information objects that areassociated with keywords in the second query, at least one of which isnot present in the first query.
 34. The method of claim 33, furthercomprising: determining a relevance score for each of the informationobjects that are associated with keywords in the second query; andsorting the information objects based on their relevance scores.
 35. Themethod of claim 32, wherein each of the association scores is a functionof a distance between connected nodes within the keyword hierarchy. 36.The method of claim 35, wherein one keyword lies at a parent node andanother keyword lies at a child node within the keyword hierarchy,further comprising: determining a first common ancestor for the parentnode and the child node.
 37. A computer system programmed to implementthe method according to claim
 32. 38. A computer-readable mediumcarrying computer-executable instructions configured to cause a dataprocessing system to perform a method of searching a database, themethod comprising: receiving a first query with one or more keywords;automatically expanding the first query to a second query to include theone or more keywords and their friend keywords having association scoresthat meet or exceed a set value; searching the database using the secondquery; and identifying a set of information objects that correspond tothe second query.
 39. The computer-readable medium of claim 38, whereinthe method further comprises: determining a relevance score for each ofthe information objects that correspond to the second query; and sortingthe information objects based on their relevance scores.
 40. Thecomputer-readable medium of claim 38, wherein the method furthercomprises: determining the association scores based at least in partupon positions of the one or more keywords and their friend keywordswithin a hierarchy of keywords.
 41. The computer-readable medium ofclaim 38, wherein the method further comprises: generating a list ofkeywords relevant to information objects residing in the database;arranging the list of keywords into one or more hierarchies of keywords,each hierarchy defining a relationship between a keyword and its friendkeywords within the hierarchy; and determining a relevance rating foreach keyword in the list of keywords, wherein the relevance ratingrepresents a degree of relevance between a keyword and an informationobject.
 42. The computer-readable medium of claim 38, wherein the methodfurther comprises: filtering at least some of the information objectsresiding in the database to meet a defined criterion.
 43. Acomputer-readable medium carrying computer-executable instructionsconfigured to cause a data processing system to perform a method offormulating a query, the method comprising: receiving a first query withone or more keywords; and automatically generating a second query thatincludes the one or more keywords and their friend keywords havingassociation scores that meet or exceed a set value; wherein theassociation scores are determined based at least in part upon positionsof the one or more keywords and their friend keywords within a keywordhierarchy.
 44. The computer-readable medium of claim 43, wherein themethod further comprises: searching a database having a repository ofinformation objects; identifying a set of information objects that areassociated with keywords in the second query, at least one of which isnot present in the first query; determining a relevance score for eachof the information objects that are associated with keywords in thesecond query; and sorting the information objects based on theirrelevance scores.
 45. The computer-readable medium of claim 43, whereineach of the association scores is a function of a distance betweenconnected nodes within the keyword hierarchy, wherein one keyword liesat a parent node and another keyword lies at a child node within thekeyword hierarchy, wherein the method further comprises: determining afirst common ancestor for the parent node and the child node.